Tornadoes cause millions of dollars in property damage and kill numerous people every year. Over the past few decades, advances have been made in forecasting areas where potential tornadic activity may develop. Tornado forecasting and detection methods include radar/satellite data and trends, models and surface analysis.
For example, the National Weather Service (“NWS”) operates a number of radars scattered throughout the country which provides weather data to subscribers. This series of radars is often referred to as NEXRAD. Subscribers, such as television stations desiring to transmit weather broadcasts, use data from the NEXRAD system to communicate certain information about storm activity.
One aspect of the NEXRAD service is the “NEXRAD ATTRIBUTES”. This service provides subscribers with detailed information concerning storms detected at each NEXRAD radar site. The NEXRAD ATTRIBUTE data includes the following information for each storm:
ID: A unique 3-digit identifier
AZ: The direction of the storm in degrees from the radar source
RANGE: The distance of the storm in nautical miles from the radar source
TVS: The presence of a tornado vortex signature (“TVS”), and if so, either a TVS or ETVS (elevated tornado vortex signature)
MESO: The presence of mesocyclonic activity, and if so, the corresponding strength
HAIL: The likelihood of hail (percent chance of hail, percent chance of severe hail, and approximate hail size in inches)
DBZM: The maximum DBZ level (a measurement of the precipitation and intensity)
FCST ANGLE: Forecasted movement angle (storm path) in degrees
FCFT MVMT: Forecasted movement speed in nautical miles per hour
A number of prior art patents have disclosed the use of the NEXRAD attributes in projecting predicted storm paths that may include tornadic activity, including U.S. Pat. Nos. 6,125,328, 6,278,947, and 6,401,039.
A number of other U.S. patents have disclosed systems and methods for detecting and displaying windshear, a condition often associated with tornadic activity. Examples of systems and methods for detecting and displaying such windshear are disclosed in U.S. Pat. Nos. 6,163,756, 6,272,433, and 6,356,843. All of these systems provide users the ability to detect and display potential tornadic activity detected by radar (either NEXRAD or privately owned Doppler radars). Additionally, other third-party weather data providers, for example, Baron Services, Inc., also utilize their own proprietary methodologies for detecting dangerous twisting of the wind and rotational activity within a storm based on radar data.
Other radar trends may be analyzed to predict when and where tornadoes may develop, for example, high reflectivity values, increasing storm strength (dBz, VIL, etc.), velocity couplets, or storm cell identification and tracking (“SCIT”) vectors. Radar data, and the display of storm conditions which may include tornadic activity derived from the radar data, offer the advantage of real time, or near real time, tracking.
A number of forecast models have also been developed to predict the potential for tornadic development over a particular area. One model used by the National Weather Service, referred to as The Rapid Update Cycle Model (“RUC”), generates a large amount of data that can be used to predict the potential for tornadic activity.
One indicator of potential tornadic activity generated by RUC is convective available potential energy (“CAPE”). CAPE data is essentially an instability index generated by the RUC. Generally, it is the amount of energy a parcel of air would have if lifted a certain distance vertically through the atmosphere. Higher numbers show more unstable and explosive environment for thunderstorms. One specific CAPE measurement is Surface-Based CAPE (J/Kg). Surface-Based CAPE is a measure of instability in the troposphere. This value represents the total amount of potential energy available to a parcel of air originating at the surface and being lifted to its level of free convection (LFC). No parcel entrainment is considered. The CAPE and CIN (discussed below) calculations use the virtual temperature correction known in the art.
CIN (Convective Inhibition) represents the “negative” area on a sounding that must be overcome before storm initiation can occur. Stated another way, CIN indicates the amount of energy that will prevent an air parcel from rising from the surface to the level of free convection. CIN is measured in negative numbers. The lower the value (−200 v. −100), then the less chance that thunderstorms are able to develop in that environment.
The higher the CAPE, the greater possibility for tornado activity. The values for CAPE often range from 0 up to approximately 8,000, with most thunderstorms registering a CAPE of between 1,000 and 4,000. It should be noted, however, that a CAPE over approximately 4,000 to 5,000 does not always result in a greater potential for tornadic activity because such a system may have too much energy and will deteriorate before a tornado is able to organize.
Another data product generated using the RUC model is storm relative helicity (SRH). SRH is a measure of the potential for cyclonic updraft rotation in right-moving supercells, and is often calculated for the lowest 1-km and 3-km layers above ground level. There is no clear threshold value for SRH when forecasting supercells, since the formation of supercells appears to be related more strongly to the deeper layer vertical shear. Larger values of 0-3-km SRH (greater than 250 m**2/s**2) and 0-1-km SRH (greater than 100 m**2/s**2), however, do suggest an increased threat of tornadoes with supercells. For SRH, larger values are generally better, but there are no clear “boundaries” between non-tornadic and significant tornadic supercells. SRH is a mathematical formula used to determine shear, and the higher the SRH, the better chance for rotation within a storm, and thus, tornadic activity. SRH values often range from 0 to over 1,000, but normally range from 0 to 500. Helicity generally measures the transfer of vorticity from the environment to an air parcel in convective motion, i.e., it shows how much shear there is in a given environment. Theoretically, higher helicity=higher shear, rotation, and turning of the atmosphere which is conductive for tornadoes.
An additional data product generated by RUC, or from data available from RUC, is the Lifting Condensation Level (“LCL”). LCL is the level at which a parcel becomes saturated. It is a reasonable estimate of cloud base height when parcels experience forced ascent. Generally, the lower the clouds, the better chance to have tornadic activity on the ground, and, thus, affect individuals and property. LCL values often range from approximately 400 to 4,000 meters. When LCL levels increase over approximately 2,000, the chances of tornadic activity on the ground are significantly decreased.
Other models disclose the moisture levels in specific areas, with areas of higher moisture, up to a certain point, the more likely to promote tornado development.
The RUC model provided by the National Weather Service is currently generated approximately once an hour, although once the data is actually provided to users, it is approximately one hour old. Thus, for the RUC model generated at 10:00 a.m., it may not be available for distribution until 11:00 a.m. The RUC model also provides forecasting data out at one hour intervals, with some RUC model data periodically available forecasted out six hours. For example, the RUC model generated at 10:00 a.m., would have data related to the current conditions at 10:00 a.m. (although not available until 11:00 a.m.), and forecasted conditions at 11:00 a.m., 12:00 p.m., and 1:00 p.m. (all not currently available until 11:00 a.m.). Although the foregoing discussion relates to the generation of CAPE, SRH, and LCL using the RUC model, other models are known in the art to generate similar values and can be used in accordance with the invention disclosed herein.
The RUC data may be downloaded from the National Centers For Environmental Prediction (“NCEP”). The information can be downloaded, and decoded to extract the information that may be of use, for example, the CAPE data, SRH data, LCL data, etc. If other models are used to generate similar data, they can either be directly input into the system or downloaded and decoded similar to the RUC model data.
In some instances, various RUC data products, or similar products from other Numerical Weather Prediction models, can be combined for tornado prediction. For example, the Storm Prediction Center of the National Weather Service utilizes an algorithm entitled “Significant Tornado Parameter” (“STP”). STP is a multiple ingredient, composite index that includes 0-6 km bulk wind difference (6BWD), 0-1 km storm-relative helicity (SRH1), surface parcel CAPE (sbCAPE), surface parcel CIN (sbCIN), and surface parcel LCL height (sbLCL). This version of STP uses fixed layer calculations of vertical shear, and the surface lifted parcels, as an alternative to the “effective layer” version of STP.
The index is formulated as follows:STP=(sbCAPE/1500 J kg−1)*((2000−sbLCL)/1500 m)*(SRH1/100 m2s−2)*(6BWD/20 m s−1)*((200+sbCIN)/150 J kg−1)
When the sbLCL is less than 1000 m AGL, the sbLCL term is set to one, and when the sbCIN is greater than −50 J kg−1, the sbCIN term is set to one. Lastly, the 6BWD term is capped at a value of 1.5, and set to zero when 6BWD is less than 12.5 m s−1. A majority of significant tornadoes (F2 or greater damage) have been associated with STP values greater than 1, while most non-tornadic supercells have been associated with values less than 1 in a large sample of RUC analysis proximity soundings.
The result of the STP is a composite model forecast of potential tornadic activity in various places across the United States. Most tornadic activity is located where the model forecast value is over one.
For most of the data products generated from RUC model data, including CAPE, SRH, LCL, and the STP, individual values are assigned to individual cells. The area of geographical interest, the continental United States in the case of the RUC, is divided into approximately 13 kilometers by 13 kilometers cells covering the United States. Thus, each individual cell will have a corresponding CAPE value, SRH value, LCL value, STP value, etc. These cells may have values according to the 10:00 forecast, 11:00 forecast, 12:00 forecast, etc. The relative size of the cells is not a limitation and different size cells can be used.
Other tornado forecasting and detection methods analyze the location and orientation of surface features, for example, strong surface lows, fronts (cold and warm), drylines, and triple points (if evident), for defining areas of strong tornado potential.
While forecast models are helpful in predicting areas of essential tornadic activity, forecast models do not take into account actual or possible tornadic activity being detected by local radars. Additionally, model data alone may indicate tornadic potential in areas that often never develop supercells due to capping or a lack of a mechanism to provide necessary forcing (front, dryline, etc.). Similarly, local radar activity may detect some evidence of tornadic activity, but it does not take into account other factors which might affect the development of tornadic activity accounted for in many of the statistical models. Moreover, radar data alone may have a hook echo or a strong velocity couplet which are associated with tornadoes, but may never produce a tornado due to a poor environment (for example, high LCL).
Additionally, during a tornado outbreak, a meteorologist may not have the time to analyze all of the available forecast model data and radar information together. Examples of data commonly missed include, a supercell moving well ahead of warm front (often having rapidly decreasing CAPE and tornado threat), presence of high LCL>2000 m (greatly decreasing tornado threat), rapidly increasing mesoscale environment south of current tornadic “action area” (thus missing even stronger storms), and marginal radar returns but quickly-spotted tornado (mesoscale potential strong at time). Thus, a system and method combining the advantages of surface models, forecasting models, and radar is needed.